research interests: change-point methods, empirical bayes estimation, genomics., model and variable selection, scan statistics, statistical modeling
Wharton Statistics Department
The aim of statistical modeling is to empower effective decision making, and the unique contribution of the field is its ability to incorporate multiple levels of uncertainty in the framing of wise decisions. Over the last few years, the development of new computational tools and the unprecedented evolution of "big data" have propelled statistical modeling to new levels. Today statistical modeling and machine learning have reached a level of impact that no large organization can afford to ignore. The information landscape is changing as it has never changed before.
At Wharton, the Department of Statistics is proud to have had a leadership role in this development. It participates in a wide range of university consortia that span the fields of computer science, finance, medicine, neuroscience, and public policy. Moreover, our faculty members have won singular international recognition for their contributions to many parts of statistical science including Bayesian analysis, game theory, high dimensional inference, information theory, machine learning, model selection, nonparametric function estimation, observational studies, probability theory, statistical algorithms, and time series analysis.